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Hands-On Neural Networks: Learn how to build and train your first neural network model using Python

Leonardo De Marchi

Laura Mitchell

R$49.99 ~~R$173.99~~

3.5 (2 Ratings)

eBook May 2019 280 pages 1st Edition

Leonardo De Marchi

Laura Mitchell

R$49.99 ~~R$173.99~~

3.5 (2 Ratings)

eBook May 2019 280 pages 1st Edition

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Key benefits

Explore neural network architecture and understand how it functions
Learn algorithms to solve common problems using backpropagation and perceptrons
Understand how to apply neural networks to applications with the help of useful illustrations

Description

Neural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial forecasting, and even machine diagnostics. Hands-On Neural Networks is designed to guide you through learning about neural networks in a practical way. The book will get you started by giving you a brief introduction to perceptron networks. You will then gain insights into machine learning and also understand what the future of AI could look like. Next, you will study how embeddings can be used to process textual data and the role of long short-term memory networks (LSTMs) in helping you solve common natural language processing (NLP) problems. The later chapters will demonstrate how you can implement advanced concepts including transfer learning, generative adversarial networks (GANs), autoencoders, and reinforcement learning. Finally, you can look forward to further content on the latest advancements in the field of neural networks. By the end of this book, you will have the skills you need to build, train, and optimize your own neural network model that can be used to provide predictable solutions.

Who is this book for?

If you are interested in artificial intelligence and deep learning and want to further your skills, then this intermediate-level book is for you. Some knowledge of statistics will help you get the most out of this book.

What you will learn

Learn how to train a network by using backpropagation
Discover how to load and transform images for use in neural networks
Study how neural networks can be applied to a varied set of applications
Solve common challenges faced in neural network development
Understand transfer learning concepts to solve tasks using Keras and Visual Geometry Group (VGG) network
Get up to speed with advanced and complex deep learning concepts such as LSTMs and natural language processing (NLP)
Explore innovative algorithms including GANs and deep reinforcement learning

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Product Details

Publication date : May 30, 2019

Length: 280 pages

Edition : 1st

Language : English

ISBN-13 : 9781788999885

Vendor :

Google

Category :

Data

Languages :

Python

Concepts :

Neural Networks

Tools :

TensorFlow

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15 Chapters

Section 1: Getting Started

Getting Started with Supervised Learning

History of AI

An overview of machine learning

Environment setup

Supervised learning in practice with Python

Feature engineering

Supervised learning algorithms

Summary

Neural Network Fundamentals

The perceptron

Keras

Feedforward neural networks

FFNN in Python from scratch

Summary

Section 2: Deep Learning Applications

Convolutional Neural Networks for Image Processing

Understanding CNNs

Convolutional layers

CNNs in Keras

Keras for expression recognition

Optimizing the network

Summary

Exploiting Text Embedding

Machine learning for NLP

Understanding word embeddings

GloVe

Summary

Working with RNNs

Understanding RNNs

Long Short-Term Memory

LSTMs in Keras

Summary

Reusing Neural Networks with Transfer Learning

Transfer learning theory

Implementing MTL

Feature extraction

Implementing TL in PyTorch

Summary

Section 3: Advanced Applications

Working with Generative Algorithms

Discriminative versus generative algorithms

Understanding GANs

GAN variations and timelines

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Customer reviews

Rating distribution

3.5

(2 Ratings)

5 star 50%

4 star 0%

3 star 0%

2 star 50%

1 star 0%

Al Oct 11, 2019

Excellent and comprehensive insight! I love the author's big picture view and how it translated it in simple python code.

Amazon Verified review

Saadallah Kassir Jan 12, 2021

- The topics discussed are interesting and useful, although some chapters need additional explanation. Some of the concepts are indeed discussed at a very high level.- The notation may be a bit hard to follow as variables are sometimes undefined. Some terminology is not technically correct, e.g., confusion between MSE and RMSE, with a confusing expression.- The code snippets may be confusing, as some instructions may be irrelevant, e.g., instructions defining variables that are never used.- The book is printed in grayscale, which makes it harder to read the figures, although the authors indicated that all the figures are available in color online.- Several typos/missing words make some paragraphs hard to read.Overall, I like the idea of the book, but it may be worth waiting for the issue of a new edition, as several errors may be confusing to the reader.

Amazon Verified review

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About the authors

Leonardo De Marchi

Leonardo De Marchi is an international speaker, author and consultant. He holds a masters in artificial intelligence (AI) and has worked as a data scientist in the sporting world, with clients such as New York Knicks, Manchester United. He now works as a head of data scientist at Badoo, the largest dating site with over 400 million users. He is also the lead instructor at ideai.io, a company specialized in Machine Learning trainings. With Ideai he provides technical and managerial training to large institutions and dynamic startups. He is also a contractor for the European Commission.

See other products by Leonardo De Marchi

Laura Mitchell

Laura Mitchell graduated with a degree in mathematics from the University of Edinburgh and, since then, has gained over 12 years' experience in the tech and data science space. She is currently lead data scientist at Badoo, which is the largest online dating site in the world with over 400 million users worldwide. Laura has hands-on experience in the delivery of projects such as NLP, image classification, and recommender systems, from initial conception through to production. She has a passion for learning new technologies and keeping up to date with industry trends.

See other products by Laura Mitchell

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Hands-On Neural Networks: Learn how to build and train your first neural network model using Python

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